As society is continuously adapting to technological change and progress, fast-moving digital transformations are the driving force for setting the necessary skillsets for the workforce. Furthermore, the advent of Industry 5.0 as a defining concept for the future, which advocates a human-centric coalescence of humans and technology or software, renders the skilled workforce the most important asset in any organization or business. The endgame of the digital transformation is to evoke the reshaping, evolution, or replacement of traditional and possibly obsolete processes at intra- or inter-organizational levels in multiple aspects, introducing innovative ways of re-defining the workforce. In this context SKILLAB will act as a smart tool for handling, honing, and widening the competencies of the personnel of companies, forecasting future skill gaps and providing European citizens with a tool for upskilling and reskilling.

Abstract There has been considerable work recently in the natural language community and elsewhere on Responsible AI. Much of this work focuses on fairness and biases (henceforth Risks 1.0), following the 2016 best seller:Weapons of Math Destruction. Two books published in 2022, The Chaos MachineandLike, Comment, Subscribe, raise additional risks to public health/safety/security such as genocide, insurrection, polarized politics, vaccinations (henceforth, Risks 2.0). These books suggest that the use of machine learning to maximize engagement in social media has created a Frankenstein Monster that is exploiting human weaknesses with persuasive technology, the illusory truth effect, Pavlovian conditioning, and Skinner’s intermittent variable reinforcement. Just as we cannot expect tobacco companies to sell fewer cigarettes and prioritize public health ahead of profits, so too, it may be asking too much of companies (and countries) to stop trafficking in misinformation given that it is so effective and so insanely profitable (at least in the short term). Eventually, we believe the current chaos will end, like the lawlessness in Wild West, because chaos is bad for business. As computer scientists, this paper will summarize criticisms from other fields and focus on implications for computer science; we will not attempt to contribute to those other fields. There is quite a bit of work in computer science on these risks, especially on Risks 1.0 (bias and fairness), but more work is needed, especially on Risks 2.0 (addictive, dangerous, and deadly).

The paper presents current work on a German corpus annotated for metaphor. Metaphors denote entities or situations that are in some sense similar to the literal referent, e.g., when “Handschrift” ‘signature’ is used in the sense of ‘distinguishing mark’ or the suppression of hopes is introduced by the verb “verschütten” ‘bury’. The corpus is part of a project on register, hence, includes material from different registers that represent register variation along a number of important dimensions, but we believe that it is of interest to research on metaphor in general. The corpus extends previous annotation initiatives in that it not only annotates the metaphoric expressions themselves but also their respective relevant contexts that trigger a metaphorical interpretation of the expressions. For the corpus, we developed extended annotation guidelines, which specifically focus not only on the identification of these metaphoric contexts but also analyse in detail specific linguistic challenges for metaphor annotation that emerge due to the grammar of German.

Abstract Many papers are chasing state-of-the-art (SOTA) numbers, and more will do so in the future. SOTA-chasing comes with many costs. SOTA-chasing squeezes out more promising opportunities such as coopetition and interdisciplinary collaboration. In addition, there is a risk that too much SOTA-chasing could lead to claims of superhuman performance, unrealistic expectations, and the next AI winter. Two root causes for SOTA-chasing will be discussed: (1) lack of leadership and (2) iffy reviewing processes. SOTA-chasing may be similar to the replication crisis in the scientific literature. The replication crisis is yet another example, like evaluation, of over-confidence in accepted practices and the scientific method, even when such practices lead to absurd consequences.

Abstract There are well-meaning efforts to address ethics that will likely make the world a better place, but care needs to be taken to avoid repeating mistakes of the past. In particular, ACL has recently introduced a new process where there are special reviews of some papers for ethics. We would be more comfortable with the new ethics process if there were more checks and balances, due process and transparency. Otherwise, there is a risk that the process could intimidate authors in ways that are not that dissimilar from the ways that academics were intimidated during the Cold War on both sides of the Iron Curtain.

We describe a state-of-the-art automatic system that can acquire subcategorisation frames from raw text for a free word-order language.We use it to construct a subcategorisation lexicon of German verbs from a large Web page corpus.With an automatic verb classification paradigm we evaluate our subcategorisation lexicon against a previous classification of German verbs; the lexicon produced by our system performs better than the best previous results.

This article describes a linguistically informed method for integrating phrasal verbs into statistical machine translation (SMT) systems.In a case study involving English to Bulgarian SMT, we show that our method does not only improve translation quality but also outperforms similar methods previously applied to the same task.We attribute this to the fact that, in contrast to previous work on the subject, we employ detailed linguistic information.We found out that features which describe phrasal verbs as idiomatic or compositional contribute most to the better translation quality achieved by our method.

Abstract An increasing number of researchers and practitioners in Natural Language Engineering face the prospect of having to work with entire texts, rather than individual sentences. While it is clear that text must have useful structure, its nature may be less clear, making it more difficult to exploit in applications. This survey of work on discourse structure thus provides a primer on the bases of which discourse is structured along with some of their formal properties. It then lays out the current state-of-the-art with respect to algorithms for recognizing these different structures, and how these algorithms are currently being used in Language Technology applications. After identifying resources that should prove useful in improving algorithm performance across a range of languages, we conclude by speculating on future discourse structure-enabled technology.

C1 - Journal Articles Refereed